Update README

README.md

@@ -47,7 +47,7 @@ tokenizer = AutoTokenizer.from_pretrained('aehrm/dtaec-type-normalizer')
 model = AutoModelForSeq2SeqLM.from_pretrained('aehrm/dtaec-type-normalizer')
 
 # Note: you CANNOT normalize full sentences, only word for word!
-model_in = tokenizer(['Freyheit', 'seyn', 'selbstthätig'], return_tensors='pt', padding=True)
+model_in = tokenizer(['Freyheit', 'seyn', 'ſelbstthätig'], return_tensors='pt', padding=True)
 model_out = model.generate(**model_in)
 
 print(tokenizer.batch_decode(model_out, skip_special_tokens=True))
@@ -60,7 +60,7 @@ Or, more compact using the huggingface `pipeline`:
 from transformers import pipeline
 
 pipe = pipeline(model="aehrm/dtaec-type-normalizer")
-out = pipe(['Freyheit', 'seyn', 'selbstthätig'])
+out = pipe(['Freyheit', 'seyn', 'ſelbstthätig'])
 
 print(out)
 # >>> [{'generated_text': 'Freiheit'}, {'generated_text': 'sein'}, {'generated_text': 'selbsttätig'}]